Volume adjusting device and adjusting method thereof

ABSTRACT

Disclosed are a volume adjusting device and an adjusting method thereof for enabling an electronic device such as a TV to perform a volume adjustment operation among operations executable by the electronic device according to a prediction model stored through machine learning based on communication with surrounding devices in a 5G communication environment. According to the present disclosure, when information indicating adjustment of the volume of an electronic device by a user is generated, volume adjustment information may be learned, and the volume may be automatically adjusted to a volume preferred by the user on the basis of the learned information when the user views a video on the electronic device.

CROSS-REFERENCE TO RELATED APPLICATION

This present application claims benefit of priority to Korean PatentApplication No. 10-2019-0092579, entitled “VOLUME ADJUSTING DEVICE ANDADJUSTING METHOD THEREOF” filed on Jul. 30, 2019, in the KoreanIntellectual Property Office, the entire disclosure of which isincorporated herein by reference.

BACKGROUND 1. Technical Field

The present disclosure relates to a volume adjusting device and anadjusting method thereof, and more particularly, to a volume adjustingdevice and an adjusting method thereof for improving convenience of auser who uses an electronic device by estimating a volume of theelectronic device preferred by the user and generating an image at theestimated volume of the electronic device.

2. Description of Related Art

The following descriptions are only for providing background informationrelated to embodiments of the present disclosure, and the descriptionsdo not necessarily constitute the prior art.

In order to control the operation of electronic devices installed in ahome, a user may directly operate the electronic devices or may remotelycontrol the electronic devices using a remote controller or the like.

In particular, there is a known technology in which a TV installed in ahome estimates noise included in a sound collected by a remotecontroller or a microphone installed in the TV, and adjusts the volumeof the TV according to the estimated noise.

That is, when receiving a TV image, noise around the TV is measured, andthe volume level of the TV is adjusted on the basis of the measurednoise. According to this automatic volume adjusting technology, the TVvolume is adjusted according to noise around the TV regardless of thetype of TV image content, and thus a user's preference that variesaccording to image content cannot be reflected.

Therefore, it is required to develop a technology for adjusting a TVvolume according to the type of content (e.g., music, broadcast, news,etc.) televised through a TV when receiving a TV image, and for learninga TV volume selected by a viewer of TV content so as to televise thecontent at a learned TV volume when the viewer reselects the TV contentthat was selected by the viewer.

As a specific example of a technology for adjusting a TV volume, KoreanPatent Application Laid-open Publication No. 10-2007-0119410, entitled“TV FOR CONTROLLING VOLUME AUTOMATICALLY AND THE METHOD THEREOF”,indicates that a broadcast signal strength is recorded, and the averagevalue of broadcast signal strengths during a certain period of time arerecorded so as to decrease a TV broadcast volume when the broadcastsignal strength is larger than the average value by a certain value.

The above “TV FOR CONTROLLING VOLUME AUTOMATICALLY AND THE METHODTHEREOF” discloses a technology for reducing auditory discomfort of auser by automatically decreasing an increased volume of an advertisementbroadcast in the middle of a TV program. However, the above documentdoes not specifically disclose a technology for adjusting a TV volumeaccording to the type of content (e.g., music, broadcast, news, etc.)televised through a TV, and for learning a TV volume selected by aviewer of TV content so as to televise the content at a learned TVvolume when the viewer reselects the TV content that was selected by theviewer.

Korean Patent Registration No. 10-1695840, entitled “SYSTEM FORAUTOMATIC CONTROLLING DIGITAL TV VOLUME BASED ON LOUDNESS AND THE METHODTHEREOF”, proposes a technology for automatically controlling a TVvolume by accurately measuring, through a multi-channel microphone,noise and reverberations generated in a broadcast viewing space.

According to the above “SYSTEM FOR AUTOMATIC CONTROLLING DIGITAL TVVOLUME BASED ON LOUDNESS AND THE METHOD THEREOF”, the volume of adigital TV is automatically controlled for each sound channel inconsideration of all of space information about a viewing environmentand noise and reverberations generated in various viewing environmentsfor viewing the digital TV, so as to provide a comfortable viewingenvironment to a user who is viewing the digital TV.

The above “SYSTEM FOR AUTOMATIC CONTROLLING DIGITAL TV VOLUME BASED ONLOUDNESS AND THE METHOD THEREOF” proposes a technology for adjusting aTV volume according to surrounding noise, but does not specificallydisclose a technology for adjusting a TV volume according to the type ofcontent (e.g., music, broadcast, news, etc.) televised through a TV, andfor learning a TV volume selected by a viewer of TV content so as totelevise the content at a learned TV volume when the viewer reselectsthe TV content that was selected by the viewer.

Therefore, it is required to develop a technology for learning a TVvolume selected by a viewer of TV content and televising the content ata learned TV volume when the viewer reselects the TV content that wasselected by the viewer, thereby automatically adjusting a TV volumeaccording to the type of content (e.g., music, broadcast, news, etc.)televised through a TV.

The above-described background art is technical information retained bythe inventor to derive the present invention or acquired by the inventorwhile deriving the present invention, and thus should not be construedas publicly known art that was known prior to the filing date of thepresent invention.

SUMMARY OF THE INVENTION

An aspect of the present disclosure is to automatically adjusting thevolume of an electronic device according to a video displayed on theelectronic device such as a TV.

Another aspect of the present disclosure is to learning informationabout the volume of an electronic device set by a viewer viewing avideo, and setting the volume of the electronic device on the basis ofthe learned information about the electronic device according to thetype (content) of a video generated through the electronic device.

In detail, the volume of the electronic device preferred by a user ofthe electronic device is estimated, and a video is played back at anestimated volume of the electronic device, so as to improve convenienceof the user of the electronic device.

Another aspect of the present disclosure is to provide a comfortableviewing environment to an electronic device user who views a video byautomatically adjusting the volume of an electronic device such as a TV.

An aspect of the present disclosure is not limited to theabove-mentioned aspects, and other aspects and advantages of the presentdisclosure, which are not mentioned, will be understood through thefollowing description, and will become apparent from the embodiments ofthe present disclosure. Furthermore, it will be understood that aspectsand advantages of the present disclosure can be achieved by the meansset forth in the claims and combinations thereof

A volume adjusting device according to an embodiment of the presentdisclosure relates to a technology for providing a comfortable viewingenvironment to an electronic device user who views a video byautomatically adjusting the volume of an electronic device such as a TV.

In detail, the volume adjusting device according to an embodiment of thepresent disclosure may include a reception unit, which receives volumeinformation about an electronic device adjusted by a user, a learningunit, which learns a correlation between the volume information aboutthe electronic device and video content displayed through the electronicdevice, a memory, which stores a prediction model of an operationexecutable by the electronic device according to a type of the videocontent displayed on the electronic device on the basis of the learnedcorrelation between the volume information about the electronic deviceand the video content, and a device control unit, which controls theelectronic device so that the predicted operation executable by theelectronic device is executed by the electronic device when the videocontent displayed through the electronic device is detected.

By using this volume adjusting device, the volume of video content maybe automatically adjusted to a volume preferred by a user.

The reception unit according to an embodiment of the present disclosuremay include a noise reception unit, which receives noise generatedaround the electronic device, a volume information reception unit, whichreceives volume adjustment information about the electronic device whenchange information about the video content displayed on the electronicdevice is generated, and a facial information reception unit, whichdetects facial information about the user adjusting a volume of theelectronic device.

That is, the noise generated around the electronic device, the volumeinformation adjusted when content is changed, and the volume informationabout the electronic device preferred by each user who adjusts thevolume may be received.

The noise reception unit according to an embodiment of the presentdisclosure may receive the noise from a point of time at which theelectronic is turned on, or receive the noise when a variation in thevolume of the electronic device is at least a preset threshold value.

In detail, when the surrounding noise is larger than the volume of avideo while the video is being displayed, the volume of the video may beadjusted higher. As described above, information about noise for whichvolume adjustment is required may be learned, and the volume may beautomatically changed to display a video without being adjusted by auser when noise which matches learned noise occurs.

The facial information reception unit of the volume adjusting deviceaccording to an embodiment of the present disclosure may receive facialinformation about the user when the electronic device is turned on orwhen a volume adjustment operation of the electronic device isperformed.

In detail, the volume of the electronic device may be adjusted when theelectronic device is operated. Therefore, the facial information aboutthe user who adjusts the volume of the electronic device when theelectronic device is operated may be received, and the received facialinformation about the user may be used as learned information.Thereafter, when a user operating the electronic device matches alearned user on the basis of the learned facial information, a video maybe displayed at a volume preferred by the learned user.

The learning unit of the volume adjusting device according to anembodiment of the present disclosure may generate a deep neural networkmodel for predicting an appropriate volume of the electronic deviceaccording to a status of noise around the electronic device, the type ofthe video content displayed on the electronic device, and the userviewing the video content, by using at least portion of the volumeinformation about the electronic device when the electronic device isturned on, the volume information about the electronic device adjustedand changed by the user when the content is changed, the type of thevideo content displayed on the electronic device, information about thecorrelation between the noise around the electronic device and thevolume information about the electronic device when the video content isdisplayed, and facial information about the user when a volume of theelectronic device is changed.

That is, the volume information about a video displayed on theelectronic device may be learned under various conditions. Inparticular, a desired volume is learned according to a user who viewsvideo content, and related video content is displayed at the volumepreferred by the user when the related video content is displayed sothat the user may listen to the video content at the preferred volume.

The volume adjusting device according to an embodiment of the presentdisclosure may include a user determination unit, which detects eitherturning on of the electronic device or changing of the video content,and determines whether a viewer viewing the video content displayed onthe electronic device is the user detected by the facial informationreception unit on the basis of the facial information, wherein when theviewer is determined to be the user, the device control unit may adjustthe volume of the electronic device to a volume suitable for the userpredicted by the deep neural network model.

In detail, when the user who is viewing a video is a previously detecteduser who changed the volume of a video, the video may be displayed at avolume preferred by the user by matching video content on the basis oflearned information.

The user determination unit of the volume adjusting device according toan embodiment of the present disclosure may detect either turning on ofthe electronic device or changing of the video content, and determinewhether there are a plurality of viewers viewing the video content onthe basis of the facial information, and when it is determined thatthere are the plurality of viewers, the device control unit may adjustthe volume of the electronic device to a volume suitable for a firstviewer among the plurality of viewers, wherein the first viewer may havea highest volume among suitable volumes predicted by the deep neuralnetwork model.

In detail, when a plurality of viewers are viewing a single channel, avideo is displayed on the basis of information indicating a highervolume at which the channel is listened to, so that the viewers maylisten to the displayed video even while having a conversation.

A method for adjusting a volume of an electronic device according to anembodiment of the present disclosure may include receiving volumeinformation about an electronic device adjusted by a user, learning acorrelation between the volume information about the electronic deviceand video content displayed through the electronic device, storing aprediction model of an operation executable by the electronic deviceaccording to a type of the video content displayed on the electronicdevice on the basis of the learned correlation between the volumeinformation about the electronic device and the video content, andcontrolling the electronic device so that the predicted operationexecutable by the electronic device is executed by the electronic devicewhen the video content displayed through the electronic device isdetected.

By using this method, the volume of video content may be automaticallyadjusted to a volume preferred by a user.

The receiving may include receiving noise generated around theelectronic device, receiving volume adjustment information about theelectronic device when change information about the video contentdisplayed on the electronic device is generated, and detecting facialinformation about the user adjusting a volume of the electronic device.

That is, the noise generated around the electronic device, the volumeinformation adjusted when content is changed, and the volume informationabout the electronic device preferred by each user who adjusts thevolume may be received.

The receiving the noise according to an embodiment of the presentdisclosure may include receiving the noise from a point of time at whichthe electronic is turned on, or receiving the noise when a variation inthe volume of the electronic device is at least a preset thresholdvalue.

In detail, when the surrounding noise is larger than the volume of avideo while the video is being displayed, the volume of the video may beadjusted higher. As described above, information about noise for whichvolume adjustment is required may be learned, and the volume may beautomatically changed to display a video without being adjusted by auser when noise which matches learned noise occurs.

The storing the facial information of the method according to anembodiment of the present disclosure may include receiving a face of theuser when the electronic device is turned on or when a volume adjustmentoperation of the electronic device is performed.

In detail, the volume of the electronic device may be adjusted when theelectronic device is operated. Therefore, the facial information aboutthe user who adjusts the volume of the electronic device when theelectronic device is operated may be received, and the received facialinformation about the user may be used as learned information.Thereafter, when a user operating the electronic device matches alearned user on the basis of the learned facial information, a video maybe displayed at a volume preferred by the learned user.

The learning of the method according to an embodiment of the presentdisclosure may include generating a deep neural network model forpredicting an appropriate volume of the electronic device according to astatus of noise around the electronic device, the type of the videocontent displayed on the electronic device, and the user viewing thevideo content, by using at least portion of the volume information aboutthe electronic device when the electronic device is turned on, thevolume information about the electronic device adjusted and changed bythe user when the content is changed, the type of the video contentdisplayed on the electronic device, information about the correlationbetween the noise around the electronic device and the volumeinformation about the electronic device when the video content isdisplayed, and facial information about the user when a volume of theelectronic device is changed.

That is, the volume information about a video displayed on theelectronic device may be learned under various conditions. Inparticular, a desired volume is learned according to a user who viewsvideo content, and related video content is displayed at the volumepreferred by the user when the related video content is displayed sothat the user may listen to the video content at the preferred volume.

The method according to an embodiment of the present disclosure mayinclude user determination including detecting either turning on of theelectronic device or changing of the video content and determiningwhether a viewer viewing the video content displayed on the electronicdevice is the user detected previously on the basis of the facialinformation, wherein the user determination may include adjusting thevolume of the electronic device to a volume suitable for the userpredicted by the deep neural network model.

In detail, when the user who is viewing a video is a previously detecteduser who changed the volume of a video, the video may be displayed at avolume preferred by the user by matching video content on the basis oflearned information.

The user determination of the method according to an embodiment of thepresent disclosure may include detecting either turning on of theelectronic device or changing of the video content, determining whetherthere are a plurality of viewers viewing the video content on the basisof the facial information, and adjusting the volume of the electronicdevice to a volume suitable for a first viewer among the plurality ofviewers when it is determined that there are the plurality of viewers,wherein the first viewer may have a highest volume among suitablevolumes predicted by the deep neural network model.

In detail, when a plurality of viewers are viewing a single channel, avideo is displayed on the basis of information indicating a highervolume at which the channel is listened to, so that the viewers maylisten to the displayed video even while having a conversation.

Other aspects, features, and advantages of the present disclosure willbecome apparent from the detailed description and the claims inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of the presentdisclosure will become apparent from the detailed description of thefollowing aspects in conjunction with the accompanying drawings, inwhich:

FIG. 1 is an exemplary diagram illustrating an electronic device volumecontrol environment including a user, a remote controller, an electronicdevice, a volume adjusting device, a server, and a network forconnecting the foregoing elements according to an embodiment of thepresent disclosure;

FIG. 2 is a schematic block diagram illustrating a volume adjustingdevice according to an embodiment of the present disclosure;

FIG. 3 is schematic block diagram illustrating a relationship betweenthe reception unit, learning unit, and memory of FIG. 2;

FIG. 4 is a diagram illustrating an example in which the volume of anelectronic device is adjusted according to an embodiment of the presentdisclosure;

FIG. 5 is a diagram illustrating an example in which the volume of anelectronic device is adjusted according to another embodiment of thepresent disclosure;

FIG. 6 is a diagram illustrating another example in which the volume ofan electronic device is adjusted according to an embodiment of thepresent disclosure; and

FIG. 7 is a schematic flowchart illustrating a volume adjusting methodaccording to an embodiment of the present disclosure.

DETAILED DESCRIPTION

Advantages and features of the present disclosure and methods forachieving them will become apparent from the descriptions of aspectsherein below with reference to the accompanying drawings. However, thepresent disclosure is not limited to the aspects disclosed herein butmay be implemented in various different forms, and should be construedas including all modifications, equivalents, or alternatives that fallwithin the sprit and scope of the present disclosure. The aspects areprovided to make the description of the present disclosure thorough andto fully convey the scope of the present disclosure to those skilled inthe art. In relation to describing the present disclosure, when thedetailed description of the relevant known technology is determined tounnecessarily obscure the gist of the present disclosure, the detaileddescription may be omitted.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The terms “comprises,” “comprising,” “including,” and“having,” are inclusive and therefore specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. Although the terms first, second, etc. may be used herein todescribe various elements, these elements should not be limited by theseterms. These terms may be only used to distinguish one element fromother elements.

Hereinafter, embodiments of the present disclosure will be described indetail with reference to the accompanying drawings. Like referencenumerals designate like elements throughout the specification, andoverlapping descriptions of the elements will not be provided.

Hereinafter, smart lighting according to an embodiment of the presentdisclosure will be described in detail with reference to theaccompanying drawings.

FIG. 1 is an exemplary diagram illustrating an electronic device volumecontrol environment including a user, a remote controller, an electronicdevice, a volume adjusting device, a server, and a network forconnecting the foregoing elements according to an embodiment of thepresent disclosure.

FIG. 1 illustrates a state in which a user 10 a, a remote controller 10b, an electronic device 20, a volume adjusting device 100, and a server30 are communicatively connected to each other by a network 40. Thevolume adjusting device 100 includes an additional communication unit 11(see FIG. 2), and thus may recognize a voice of the user 10 a via thewired or wireless network 40, or may transmit and receive remoteinformation of the remote controller 10 b via the server 30.

The volume adjusting device 100, the user 10 a, the remote controller 10b, and the server 30 may be connected to each other in a 5Gcommunication environment. Furthermore, in addition to the devicesillustrated in FIG. 1, various electronic devices used in home or officemay be connected to each other to operate in an environment of theInternet of things.

The electronic device 20 may be one of various electronic devices usedin home or office, and is exemplarily described as a TV below. However,it is obvious that the electronic device 20 is not limited to a TV, andmay be any one of devices (e.g., a projector or the like) for viewingimages and devices (e.g., a washing machine, a refrigerator, or thelike) for generating a result of an operation performed by a device as avoice.

The volume adjusting device 100 may receive a speech uttered by the user10 a, and may recognize and analyze the speech of the user to provide arelated service. To this end, the volume adjusting device 100 mayinclude an artificial intelligence (AI) speaker, and may serve as a hubfor controlling an electronic device having no voice input/outputfunction.

For example, the speech uttered by the user 10 a may be a specificcommand for activating a voice function of the volume adjusting device100, and may be referred to as a wake-up word. For example, the speechuttered by the user 10 a may be a command such as “turn the TV volumeup” or “turn the TV volume down”. Such a command may be preset andstored in a memory 160 described below.

The user 10 a may utter a speech related to operation information aboutthe electronic device 20. Here, the speech uttered by the user 10 a maybe referred to as, for example, a command for turning on/off theelectronic device 20, a command for adjusting a volume of the electronicdevice 20, or the like.

Furthermore, a command for operating the electronic device 20 may begenerated by the remote controller 10 b rather than the voice of theuser 10 a. To this end, the remote controller 10 b and the electronicdevice 20 may be connected to each other in a 5G communicationenvironment as described above.

In a specific example, if the user 10 a utters the wording “turn the TVvolume up” towards the volume adjusting device 100 when the user 10 adesires to adjust the volume of a TV among the electronic devices 20 byvoice or the remote controller 10 b, a command of “TV volume increase”may be received by the server 30. Therefore, the volume of the TV may beincreased, and increased TV volume information (e.g., volume level) maybe stored in the memory 160.

Likewise, the volume of the TV may be increased using a volume button ofthe remote controller 10 b other than the voice of the user 10 a, and anoperation of pressing the volume button may allow the command of “TVvolume increase” to be received by the server 30. Therefore, the volumeof the TV is increased, and increased TV volume information (e.g.,volume level) is stored in the memory 160.

Here, the remote controller 10 b may be one of devices capable ofremotely controlling each electronic device 20, and it would be obviousthat any device capable of remotely controlling the electronic device 20may be used.

The server 30 may be a database server, which provides big data requiredfor applying a variety of artificial intelligence algorithms and datarelated to voice recognition. Furthermore, the server 30 may include aweb server or application server for remotely controlling the remotecontroller 10 b and the voice of the user 10 a by using an applicationor a web browser installed in a user terminal (not shown) (e.g., amobile terminal, a wearable device, or the like).

Artificial intelligence (AI) is an area of computer engineering andinformation technology that studies how to make computers perform thingshumans are capable of doing with human intelligence, such as reasoning,learning, self-improving, and the like, or how to make computers mimicsuch intelligent human behaviors.

In addition, artificial intelligence does not exist on its own, but israther directly or indirectly related to a number of other fields incomputer science. In recent years, there have been numerous attempts tointroduce an element of AI into various fields of information technologyto solve problems in the respective fields.

Machine learning is an area of artificial intelligence that includes thefield of study that gives computers the capability to learn withoutbeing explicitly programmed. More specifically, machine learning is atechnology that investigates and builds systems, and algorithms for suchsystems, that are capable of learning, making predictions, and enhancingits own performance on the basis of experiential data. Machine learningalgorithms, rather than executing rigidly set static program commands,may take an approach that builds a specific model based on input datafor deriving a prediction or decision.

The server 30 may receive an electronic device control command generatedin the remote controller 10 b and the voice of the user 10 a, and maypredict an operation executable by the electronic device 20 andcorresponding to the received electronic device control command. Here,the operation executable by the electronic device 20 represents, forexample, an operation of increasing or decreasing a volume. That is, aprocess of adjusting the volume of the electronic device 20 may beexecuted by the server 30.

The network 40 may serve to connect the volume adjusting device 100 andthe electronic device 20. The network 40 includes, but is not limitedto, wire-based networks such as LANs (local area networks), wide areanetworks (WANs), metropolitan area networks (MANs), and integratedservice digital networks (ISDNs); or wireless networks such as wirelessLANs, CDMA, Bluetooth communications, satellite communications, and soforth. Also, the network 40 may transmit or receive data usingshort-range communication and/or long-range communication technologies.Examples of the short-range communication technologies may includeBluetooth, radio frequency identification (RFID), infrared dataassociation (IrDA), ultra-wideband (UWB), ZigBee, and wireless fidelity(Wi-Fi). Examples of the long-range communication technologies mayinclude code division multiple access (CDMA), frequency divisionmultiple access (FDMA), time division multiple access (TDMA), orthogonalfrequency division multiple access (OFDMA), and single carrier frequencydivision multiple access (SC-FDMA).

The network 40 may include connection of network elements such as hubs,bridges, routers, switches, and gateways. The network 40 may include oneor more connected networks, including a public network such as theInternet, as well as a private network such as a secure corporateprivate network, for example, a multiple network environment. Access tothe network 30 may be provided through one or more wire-based orwireless access networks. Furthermore, the network 40 may support theInternet of things (IoT) for exchanging and processing informationbetween distributed elements such as things or the like and/or 5Gcommunication.

FIG. 2 is a schematic block diagram illustrating a volume adjustingdevice according to an embodiment of the present disclosure, and FIG. 3is schematic block diagram illustrating a relationship between thereception unit, learning unit, and memory of FIG. 2. Descriptions whichoverlap with the above descriptions related to FIG. 1 are not providedbelow.

Referring to FIGS. 2 and 3, the volume adjusting device 100 may includea communication unit 110, an input unit 120, a reception unit 130, atransmission unit 140, a learning unit 150, a memory 160, a userdetermination unit 170, and a device control unit 180.

The communication unit 110 may interwork with the user 10 a and theremote controller 10 b to provide a communication interface required forproviding, in a form of packet data, an electronic device controlcommand signal input to the remote controller 10 b.

Furthermore, the communication unit 110 may serve to receive apredetermined information request signal from the electronic device 20and/or the remote controller 10 b, and may serve to process a speechuttered by the user 10 a and transmit the processed speech to theelectronic device 20. Furthermore, the communication unit 110 may be adevice including hardware and software required fortransmitting/receiving signals such as a control signal and a datasignals via a wire/wireless connection to another network device.

In the present embodiment, another electronic device may represent anelectronic device or the like which expresses, by voice, an operation ofa home appliance not having a voice input/output function, such as, anair conditioner, a refrigerator, a washing machine, or the like.

The input unit 120 may include a voice input unit and a button inputunit. This input unit 120 may input a control command of the electronicdevice 20 via a button or a user's voice. For example, when the user 10a utters a control command for controlling the volume of the TV, thecontrol command of the TV may be input via the voice input unit, and,when the control command for controlling the volume of the TV is inputvia the remote controller 10 b, the control command of the TV may beinput via the button input unit.

To this end, the voice input unit may include at least one microphone(not shown). Furthermore, the voice input unit may include a pluralityof microphones (not shown) for more accurately receive a voice of theuser 10 a. Here, the plurality of microphones may be arranged spacedapart from each other at different indoor positions, and may process areceived voice signal of the user 10 a into an electric signal.

In addition, the input unit 120 may use various noise eliminationalgorithms to eliminate noise generated while receiving the speech ofthe user. Furthermore, the input unit 120 may include various elementsfor processing a voice signal, such as a filter (not shown) foreliminating noise when receiving the speech of the user and an amplifier(not shown) for amplifying and outputting a signal output from thefilter.

Furthermore, the input unit 120 may convert the voice signal of the user10 a input via the input unit 120 into a text, and may extract from thetext a control command for controlling the volume of the TV. The inputunit 120 may select any one word from the extracted control command onthe basis of a prestored word. When any one word is selected, thecontrol unit 190 may execute the control command processed by the inputunit 120 as a control command for controlling the volume of the TV, and,as a result, the volume of the TV may be adjusted according to thecontrol command.

The reception unit 130 may communicate with the server 30, and mayreceive information about the volume of the electronic device 20adjusted by the user 10 a. In the present embodiment, the reception unit130 may include a noise reception unit 132 for receiving noise generatedaround the electronic device 20, a volume information reception unit 134for receiving volume adjustment information about the electronic device20 when a channel is changed in the electronic device 20, and a facialinformation reception unit 136 for storing facial information about theuser who adjusts the volume of the electronic device 20.

In detail, the noise reception unit 132 may receive noise generatedaround the TV. For example, while viewing and listening to a displayedvideo, the user may suddenly increase the volume of the video due tonoise coming from the outside. Here, the noise reception unit 132 mayreceive the noise generated around the TV to determine a level of noiseat which the TV volume is increased.

To this end, the noise reception unit 132 may receive noise from aturn-on time at which power is supplied to the TV or a screen of the TVis turned on, or may receive noise when a variation in the volume of theelectronic device 20 is at least a preset threshold value.

In detail, the noise reception unit 132 may receive noise during anentire time when the user views a video. Here, when the user rapidlyincreases a volume, a variation in the level of received noise may becompared. Thereafter, when it is determined that the volume is increasedas the level of noise increases, related noise information may betransmitted to the learning unit 150 via the transmission unit 140.

Here, the noise information transmitted to the learning unit 150 may bea reference value based on a threshold value at which the user who isviewing the TV increases the TV volume. By learning this noiseinformation that increases the TV volume, the TV volume may beautomatically adjusted when related noise is received, so as not to giveinconvenience to the user even if noise occurs.

The volume information reception unit 130 may receive volume informationabout a channel changed by the user. For example, it is assumed that theuser is viewing a news channel, and sets the TV volume to volume level 3when viewing the news channel. Thereafter, the user may change a channelfrom the news channel to a music channel, and may change the TV volumeto volume level 6. As described above, TV volume information based on achannel change may be received, and the received information may betransmitted to the learning unit 150 via the transmission unit 140 andmay be learned by the learning unit 150, so as to be used toautomatically change the TV volume when the user changes a channel fromthe news channel to the music channel.

The facial information reception unit 136 may receive a face of the userwho changes a channel. The received facial information about the usermay be used to automatically adjust the volume according to differentusers who view the same type of content at different volumes.

That is, the volume set by user 1 who views a news channel may be volumelevel 3, but the volume set by user 2 who views the same news channelmay be volume level 5. Therefore, since the faces of users are stored,the volume may be automatically set to volumes corresponding to theusers according to stored user information even if the same channel isviewed by the users.

Here, the facial information reception unit 136 may store the face ofthe user at a point of time at which the electronic device is turned onor a point of time at which the volume of the electronic device 20 isadjusted. In detail, the user may adjust the volume of the TV whileturning on the TV which is in an off state. At this moment, the face ofthe user adjusting the volume of the TV may be stored. In another case,the user may change a channel in a state in which the TV is turned on,and may adjust the volume according to a changed channel. At thismoment, the face of the user who has adjusted the volume whileattempting to change a channel may be stored.

The stored face of the user may be learned by the learning unit 150,and, thereafter, by using learned information, the volume of the TV maybe automatically adjusted according to channel information and the userwho adjusts the volume of the TV.

As described above, when the volume information about the electronicdevice 20 is received, the received information may be learned via thelearning unit 150. In detail, the learning unit 150 may learn acorrelation between the volume information about the electronic device20 and video content generated and displayed through the electronicdevice 20.

Here, the content is exemplarily described as a TV channel in the aboveembodiment, but the TV channel may specifically represent any one typeof a broadcast such as a music broadcast, news, movie, soap opera,entertainment, advertisement, etc. Such information may be attached tovideo data as additional information so as to be received. According tosuch content, the learning unit 150 learns the correlation between thetype of a broadcast and a volume at which the broadcast is listened toand the correlation between the type of a broadcast and anincrease/decrease in the volume.

For example, the learning unit 150 may generate a deep neural networkmodel for predicting an appropriate volume of an electronic deviceaccording to a status of noise around the electronic device 20, the typeof video content displayed in the electronic device, and the userviewing the video content, by using at least portion of informationabout the volume of the electronic device 20 when the electronic device20 is turned on, information about the volume of the electronic device20 adjusted and changed by the user when content is changed, the type ofvideo content displayed through the electronic device 20, informationabout the correlation between noise around the electronic device 20 andthe volume information about the electronic device 20 when video contentis displayed, and the facial information about the user 10 a when thevolume of the electronic device 20 is changed.

In detail, in the case of the above example, when the noise receptionunit 132 receives noise generated around the TV, the degree of a TVvolume adjusted by the user according to the magnitude of the receivednoise may be learned. For example, a variation in the magnitude of thenoise received by the noise reception unit 132 may be equal to or largerthan a preset threshold value. That is, when the user feels as if thesurrounding noise is larger than the volume of the TV, the userincreases the volume of the TV. The learning unit 150 may learninformation about noise for which the user increases the volume of theTV. On the basis of this learned information, the device control unit180 may automatically increase the volume of the TV when the noise forwhich the user increases the volume of the TV occurs.

Furthermore, in another embodiment, the volume information receptionunit 130 may receive volume information about a channel changed by theuser. For example, it may be assumed that the user sets the volume tovolume level 3 to view a news channel. Here, when the user changes achannel from the news channel to a music channel, the user may set thevolume to volume level 6 to view the music channel. This TV volumeinformation based on a channel change may be learned by the volumeinformation reception unit 130. Here, when the user changes a channel tothe music channel, the TV volume may be changed on the basis of thelearned volume information, and thus the user may view the channel at apreferred volume.

In another embodiment, the face of the user around the TV may be learnedfrom a point of time at which the TV is turned on and/or the face of theuser may be learned when a channel is changed. In detail, the user mayadjust the volume of the TV while operating the TV. At this moment, theface of the user adjusting the TV volume may be received by the facialinformation reception unit 136, and when TV is re-operated and it isdetermined that the received face matches the learned face of the userin a state in which the face of the user has been learned, the volume ofthe TV is set to a volume preferred by the user whose face has beenlearned. Meanwhile, the user may adjust the volume of the TV whenchanging a channel (e.g., news channel->music channel). Here, the faceof the user adjusting the volume may be learned, and when it isdetermined that the channel is changed from a news channel to a musicchannel, and the user who has changed the channel is a pre-learned user,the volume of the TV may be automatically adjusted on the basis ofstored TV volume information.

To this end, the volume adjusting device 100 may include an image sensorfor recognizing the face of the user, a proximity sensor for recognizingthe user near the TV, etc. In detail, the proximity sensor may obtainlocation data of the user (object) positioned near the volume adjustingdevice 100 using infrared light or the like. The image sensor mayinclude at least one camera for capturing an image or shooting a videoof surroundings of the volume adjusting device 100.

The volume adjusting device 100 may further include the userdetermination unit 170 for determining whether a learned face of theuser matches a stored face of the user. That is, when it is detectedthat the electronic device 20 is turned on or the user changes videocontent (e.g., changes a channel), the user determination unit 170 maydetermine whether a viewer who is viewing video content displayed on theelectronic device 20 is the user detected by the facial informationreception unit 136 on the basis of the facial information.

When it is determined that the viewer who is viewing the video contentis the user adjusting the volume of the electronic device 20, the devicecontrol unit 180 may adjust the volume of the electronic device 20 to avolume suitable for the user predicted by the deep neural network model.

In detail, the facial information reception unit 136 and the volumeinformation reception unit 134 may receive the face of viewer 1 who isviewing a music broadcast and a volume at which viewer 1 listens to themusic broadcast. Thereafter, when the TV is changed from an off state toan on state, and it is determined that the user near the TV is viewer 1on the basis of a video and/or image obtained by the image sensor, theTV volume may be set to the volume at which viewer 1 listened to themusic broadcast.

Here, the user determination unit 170 detects either turning on of theelectronic device 20 or changing a channel of the electronic device 20,but there may be a plurality of users who change a channel. In thiscase, a priority may be given to the user requiring volume adjustment soas to adjust the volume of the electronic device 20. In detail, when twoor more viewers are viewing the TV, the volume of the electronic device20 may be adjusted on the basis of information about a first viewer, theinformation indicating that a channel is listened to at a high volume.

This user determination unit 170 may be any one of devices capable ofdetermining whether a stored user image matches a user image obtainedfrom the image sensor.

The memory 160 may store a prediction model of an operation executableby the electronic device 20 between the volume information about theelectronic device 20 and a channel of a video displayed through theelectronic device 20. In detail, the memory 160 may store the predictionmodel of an operation executable by the electronic device 20 on thebasis of any one among information about the volume of the electronicdevice 20 when the electronic device 20 is turned on, information aboutthe volume of the electronic device 20 when a channel is changed, andinformation about the volume of the electronic device 20 while a videois displayed through the electronic device 20. To this end, the memory160 may include a model generation unit 162 and a database 164 forgenerating an operation executable by the electronic device 20, in amanner that is stored in the memory 160.

For example, when the user who is a TV viewer viewing a music channelutters the wording “turn up the TV volume”, the term “volume” may be alanguage for selecting a setting of the TV, and the term “turn up” maybe a command for controlling the volume of the TV. As described above,when the user utters the wording “turn up the volume”, a volume settingof the TV may be changed, and information about a channel for which thevolume setting is changed is stored.

In detail, the memory 160 may store a prediction model for automaticallyincreasing the TV volume when the level of noise generated around the TVis equal to or larger than a preset threshold value and the noise havinga level equal to or larger than the threshold value is received.

According to the above embodiment, the generated noise may be areference value based on a threshold value at which the user who isviewing the TV increases the TV volume. By learning this noiseinformation that increases the TV volume, the TV volume may beautomatically adjusted when related noise is received, so as not to giveinconvenience to the user even if noise occurs.

Furthermore, the memory 160 may store information indicating that thevolume is set to volume level 3 on the basis of minimum volume level 0when a news channel is viewed, and may store a prediction model forautomatically adjusting the volume of the TV to prestored volume level 6when the user changes a channel from a news channel to a music broadcastchannel according to information indicating that the volume is set tovolume level 6 when a music broadcast is viewed.

Furthermore, the memory 160 may store a prediction model for storing thevolume of the electronic device 20 according to the face of the userstored in the facial information reception unit 136. In detail, the usermay set the volume of the TV to volume level 3 while operating the TV.Likewise, the user may adjust the volume of the TV from volume level 3to volume level 6 when changing a channel (e.g., news channel->musicchannel). As described above, a prediction model may be stored, whichautomatically adjusts the volume of the TV when a channel of the TV ischanged in a state in which the user who changes a channel is learned onthe basis of the face of the user adjusting the volume of the TV andinformation indicating a situation in which the volume is adjusted.

Here, the memory 160 stores a prediction model for adjusting the TVvolume corresponding to pre-learned contents according to a speechcommand of the user 10 a according to channel.

The memory 160, which stores a variety of information required foroperating the server 30 in addition to the prediction model, may includea volatile or non-volatile recording medium. The recording medium isconfigured to store data readable by the control unit 190, and mayinclude a hard disk drive (HDD), solid state disk (SSD), silicon diskdrive (SDD), ROM, RAM, CD-ROM, a magnetic tape, a floppy disk, a lightdata storage device, and the like.

The memory 160 may store limited data. For example, the memory 160 maystore a preset language for determining a control command from a speechuttered by the user and/or the remote controller 10 b. As describedabove, the term “volume” included in the wording “turn up the TV volume”uttered by the user may be set as a language for matching the electronicdevice 20, and the term “turn up” may be set as a language for changingan operation setting (e.g., TV volume increase) of the electronicdevice. This control command may be preset by the prediction model asdescribed above, but may be set and changed by the user.

As described above, when an operation executable by the electronicdevice 20 based on the prediction model is stored, and video display isdetected through the electronic device 20, the device control unit 180may be controlled so that the operation executable by the electronicdevice 20 is executed by the electronic device 20.

In detail, when the user sets a channel of the TV in a state in whichthe memory 160 stores an operation executable by the TV between achannel and the TV as described above, the volume of the TV is adjustedon the basis of the prestored prediction model.

The control unit 190 may control a command signal input to the volumeadjusting device 100 so as to automatically adjusting the volume of theTV according to the correlation between a channel and learned volumeinformation about the TV. The control unit 190, which is a type of acentral processing unit, may provide various functions for adjusting thevolume of the TV by driving control software installed in the memory160.

Here, the control unit 190 may include any type of devices capable ofprocessing data, such as a processor. Here, the ‘processor’ may refer toa data processing device built in a hardware, which includes physicallystructured circuits in order to perform functions represented as a codeor command contained in a program. Examples of the data processingdevice built in a hardware include, but are not limited to, processingdevices such as a microprocessor, a central processing unit (CPU), aprocessor core, a multiprocessor, an application-specific integratedcircuit (ASIC), a field programmable gate array (FPGA), and the like.

The server 30 of the present embodiment may perform machine learningsuch as deep learning or the like in response to an electronic devicecontrol command input by the user. To this end, the memory 160 may storeresult data or the like used in the machine learning.

Deep learning, which is a subfield of machine learning, enablesdata-based learning through multiple layers. As the number of layers indeep learning increases, the deep learning network may acquire acollection of machine learning algorithms that extract core data frommultiple datasets.

Deep learning structures may include an artificial neural network (ANN),and may include a deep neural network (DNN) such as a convolutionalneural network (CNN), a recurrent neural network (RNN), a deep beliefnetwork (DBN), and the like. The deep learning structure according tothe present embodiment may use various structures well known in the art.For example, the deep learning structure according to the presentdisclosure may include a CNN, an RNN, a DBN, and the like. The RNN,which is frequently used for processing natural language, is anefficient structure for processing time-series data that varies withtime, and may constitute an artificial neural network by stacking layersup every moment. DBN includes a deep learning structure formed bystacking up multiple layers of restricted Boltzmann machines (RBM),which is a deep learning scheme. A DBN has the number of layers formedby repeating RBM training. CNN includes a model mimicking a human brainfunction, built on the assumption that when a person recognizes anobject, the brain extracts basic features of the object and recognizesthe object based on the results of complex processing in the brain.

Meanwhile, the artificial neural network can be trained by adjustingconnection weights between nodes (if necessary, adjusting bias values aswell) so as to produce desired output from given input. Also, theartificial neural network can continuously update the weight valuesthrough learning. Furthermore, methods such as back propagation may beused in training the artificial neural network.

As described above, the server 30 may be provided with an artificialneural network and perform machine learning-based user recognition anduser's voice recognition using received audio input signals as inputdata.

The controller 190 may include an artificial neural network, forexample, a deep neural network (DNN) such as CNN, RNN, DBN, and thelike, and may train the DNN. Both unsupervised learning and supervisedlearning may be used as a machine learning method of the artificialneural network. Specifically, the control unit 190 may control such thata voice color recognition artificial neural network structure is updatedafter training according to settings.

FIG. 4 is a diagram illustrating an example in which the volume of anelectronic device is adjusted according to an embodiment of the presentdisclosure. Descriptions which overlap with the above descriptionsrelated to FIGS. 1 to 3 are not provided below.

FIG. 4A illustrates an example in which a voice command of the user 10 ais input through the input unit 120 when the user 10 a utters the voicecommand while viewing a music channel. Here, the user may utter, forexample, the wording “turn up the TV volume”.

FIG. 4B illustrates an example in which the voice command uttered by theuser is learned. For example, on the assumption that the volume of theTV has been set to volume level 3 previously (FIG. 4A), the volume ofthe TV may be gradually increased to volume level 4 or higher to displaya video since the user utters the wording “turn up the TV volume”. Here,since the volume of the TV is gradually increased, the user may utterthe wording “turn down the TV volume” or may utter a volume increasestop command such as “stop turning up the volume”. For example, on theassumption that the volume of the TV is volume level 6 when the userutters the wording “stop turning up the volume”, the memory 160 of thevolume adjusting device 100 may store a learning result that the volumepreferred by the user who views a music channel is volume level 6.

FIG. 4C illustrates an example in which TV volume information about aspecific channel is stored, and when the specific channel is selected, avideo is displayed at a stored TV volume. That is, when the user selectsa music channel according to the above embodiment, the volume of the TVis set to volume level 6 on the basis of learned information so as todisplay a music broadcast.

FIG. 5 is a diagram illustrating an example in which the volume of anelectronic device is adjusted according to another embodiment of thepresent disclosure. Descriptions which overlap with the abovedescriptions related to FIGS. 1 to 4 are not provided below.

The following descriptions are provided on the assumption that it hasbeen learned that the user sets the volume of the TV to volume level 6when the user views a music channel as described above with reference toFIG. 4, and, likewise, the user sets the volume of the TV to volumelevel 3 when the user views a news channel.

FIG. 5A illustrates an example in which the user 10 a views a musicchannel. Here, when the user changes a channel from the music channel toa news channel, the user may generate a channel change command using avoice command and/or a remote controller.

FIG. 5B illustrates an example in which a channel of the TV is changed,and a channel video is displayed at a learned volume according to thechanged channel. That is, the video displayed in FIG. 5A is a musicchannel, and it has been learned that the user sets the volume to volumelevel 6 when viewing the music channel. Here, when the channel ischanged to the news channel, it is determined that the user listens tothe TV at volume level 3 according to pre-learned information, and thevolume of the TV may be reduced to display a video.

FIG. 6 is a diagram illustrating another example in which the volume ofan electronic device is adjusted according to an embodiment of thepresent disclosure. Descriptions which overlap with the abovedescriptions related to FIGS. 1 to 5 are not provided below.

FIG. 6 illustrates that two viewers (a first viewer 10 a(1), a secondviewer 10 a(2)) are viewing a music channel. Here, the volume adjustingdevice 100 may learn that the first user 10 a(1) sets the volume of theTV to volume level 6 when viewing a music channel, and the second user10 a(2) sets the volume of the TV to volume level 2 when viewing themusic channel.

In this case, the volume adjusting device 100 may display a video on thebasis of learned information indicating a higher volume at which a videois displayed. Here, the volume adjusting device 100 may preferentiallydetermine whether there are a plurality of viewers viewing the TV byusing the user determination unit 170, and may set the volume of the TVon the basis of information pertaining to a user who listens to achannel at a higher volume among the users.

Therefore, when a plurality of viewers are viewing a single channel, avideo is displayed on the basis of information indicating a highervolume at which the channel is listened to, so that the viewers maylisten to the displayed video even while having a conversation.

Meanwhile, it has been exemplarily described that when a plurality ofusers are viewing a single channel, the volume of the TV is adjusted onthe basis of the volume information about a user who listens to thechannel at a higher volume in an embodiment of the present disclosure,but it would be obvious that the volume of the TV may be adjusted on thebasis of the volume information about a user who listens to the channelat a lower volume.

FIG. 7 is a schematic flowchart illustrating a volume adjusting methodaccording to an embodiment of the present disclosure. In the followingdescriptions, the elements referred to by the same reference numerals asthose illustrated in FIGS. 1 to 6 are assumed to be the same elements asillustrated in FIGS. 1 to 6, and are thus not described in detail.

Referring to FIG. 7, the user 10 a who views a TV for displaying a videoamong the electronic devices 20 may turn on the TV, or may select achannel to be viewed from a turned on TV (S110).

Here, information about the channel being viewed by the user who isviewing the TV and volume information about the channel may be collected(S120). The information about the channel may represent informationabout a channel being viewed, such as a music channel, a news channel, asports channel, or the like. The volume information about the channelmay represent volume level information about a channel being viewed. Forexample, “the user is currently viewing a music channel (channelinformation, and the volume of the channel being viewed is set to volumelevel 6 (volume level information)” may be the channel information andthe volume information about a channel.

When the channel information and the channel volume information arecollected, the collected channel information and volume information maybe learned (S130). In detail, during a learning operation, thecorrelation between the volume information about the TV and theinformation about the channel through which a video generated via the TVis displayed may be learned.

For example, any one among information about the volume of theelectronic device 20 when the electronic device 20 is turned on,information about the volume of the electronic device 20 when a channelis changed, information about the volume of the electronic device 20while a video is being displayed through the electronic device 20,information about the correlation between noise generated around theelectronic device 20 and the volume information about the electronicdevice 20 when a video is displayed, and facial information about theuser 10 a when the volume of the electronic device 20 is changed may belearned.

According to the above example, when the noise generated around the TVis received, the degree of a TV volume adjusted by the user according tothe magnitude of the received noise may be learned. Furthermore, thevolume information about a channel changed by the user may be received,and the received volume information may be learned. Furthermore, theface of the user around the TV may be learned from a point of time atwhich the TV is turned on and/or the face of the user may be learnedwhen a channel is changed.

Here, a volume preferred by the user when the user views the TV may beestimated according to a situation (S131). In detail, it may be assumedthat the user increases the TV volume when the noise generated aroundthe TV has at least a certain level of decibel (e.g., 65 dB). Forexample, it may be assumed that the user increases the volume of the TVby two levels when noise having at least a certain level is generated.That is, it may be estimated that the user prefers to increase thevolume by two levels to view the TV when noise having at least a certainlevel is generated.

Furthermore, it may be assumed that the user sets the volume to volumelevel 3 to view a news channel. Thereafter, the user may change achannel from the news channel to a music channel, and may change the TVvolume to volume level 6. According to the TV volume information basedon a channel change, it may be estimated that the user prefers volumelevel 6 for the music channel and prefers volume level 3 for the newschannel.

Here, when a preferred volume according to a channel is estimated, itmay be determined whether the user additionally adjusts the volume ofthe TV (S140). That is, even after the volume of the TV is automaticallyadjusted according to a channel, the user may manually adjust the volumeof the TV. This is because there may be two or more viewers allowed toview the TV in a home or office, and the viewers may have differentpreferred volumes for the same channel. When the volume of the TV ismanually adjusted by the user, the adjusted TV volume may be re-learned.

Here, channel volume information according to users may be learned byrecognizing the faces of the users, so that the volume of the TV may beset to different volumes when a first viewer views a music channel and asecond viewer views the music channel.

Thereafter, when a specific channel (e.g., music channel) is selected,the volume of the TV may be adjusted on the basis of learned volumeinformation so as to display (S150). Here, when two or more viewers areviewing the TV, and are viewing a single channel simultaneously, thevolume of the TV may be adjusted on the basis of volume informationabout a viewer who listens to the channel at a higher volume. When thetwo or more viewers view the TV at different times, the volume of the TVmay be adjusted on the basis of the volume information about each of theviewers so as to display.

Accordingly, the volume of the TV may be automatically adjustedaccording to a listening condition of the user so as to display a video,thereby providing a more comfortable viewing environment to the user.

According to the present disclosure, a preferred volume may be learnedaccording to video content such as a movie, soap opera, advertisement,entertainment, music, news, or the like, and when video content isselected, the video content is displayed on the basis of learnedinformation, so that the video may be listened to at a volume desired bya user.

Furthermore, according to the present disclosure, the volume of a devicefor displaying a video, such as a TV, may be automatically adjusted to avolume preferred by a user viewing video content according to the typeof the video content, so as to provide a comfortable viewing environmentto the user who uses the device for displaying a video.

Furthermore, according to the present disclosure, when a plurality ofviewers are viewing a single piece of video content, a video isdisplayed on the basis of information indicating a higher volume atwhich the video is listened to, so that the viewers may listen to thedisplayed video even while having a conversation.

The example embodiments described above may be implemented throughcomputer programs executable through various components on a computer,and such computer programs may be recorded in computer-readable media.Examples of the computer-readable media include, but are not limited to:magnetic media such as hard disks, floppy disks, and magnetic tape;optical media such as CD-ROM disks and DVD-ROM disks; magneto-opticalmedia such as floptical disks; and hardware devices that are speciallyconfigured to store and execute program codes, such as ROM, RAM, andflash memory devices.

The computer programs may be those specially designed and constructedfor the purposes of the present disclosure or they may be of the kindwell known and available to those skilled in the computer software arts.Examples of program code include both machine code, such as produced bya compiler, and higher level code that may be executed by the computerusing an interpreter.

As used in the present application (especially in the appended claims),the terms ‘a/an’ and ‘the’ include both singular and plural references,unless the context clearly states otherwise. Also, it should beunderstood that any numerical range recited herein is intended toinclude all sub-ranges subsumed therein (unless expressly indicatedotherwise) and therefore, the disclosed numeral ranges include everyindividual value between the minimum and maximum values of the numeralranges.

Also, the order of individual steps in process claims of the presentdisclosure does not imply that the steps must be performed in thisorder; rather, the steps may be performed in any suitable order, unlessexpressly indicated otherwise. In other words, the present disclosure isnot necessarily limited to the order in which the individual steps arerecited. The steps included in the methods according to the presentdisclosure may be executed by a process or modules for executingfunctions of corresponding steps. All examples described herein or theterms indicative thereof (“for example”, etc.) used herein are merely todescribe the present disclosure in greater detail. Therefore, it shouldbe understood that the scope of the present disclosure is not limited tothe example embodiments described above or by the use of such termsunless limited by the appended claims. Also, it should be apparent tothose skilled in the art that various alterations, permutations, andmodifications may be made within the scope of the appended claims orequivalents thereof

The present disclosure is thus not limited to the example embodimentsdescribed above, and rather intended to include the following appendedclaims, and all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the following claims.

What is claimed is:
 1. A volume adjusting device for automaticallyadjusting a volume, the volume adjusting device comprising: a receptionunit configured to receive volume information about an electronic deviceadjusted by a user; a learning unit configured to learn a correlationbetween the volume information about the electronic device and videocontent displayed through the electronic device; a memory configured tostore a prediction model of an operation executable by the electronicdevice according to a type of the video content displayed on theelectronic device on the basis of the learned correlation between thevolume information about the electronic device and the video content;and a device control unit configured to control the electronic device sothat the predicted operation executable by the electronic device isexecuted by the electronic device when the video content displayedthrough the electronic device is detected.
 2. The volume adjustingdevice of claim 1, wherein the reception unit comprises: a noisereception unit configured to receive noise generated around theelectronic device; a volume information reception unit configured toreceive volume adjustment information about the electronic device whenchange information about the video content displayed on the electronicdevice is generated; and a facial information reception unit configuredto detect facial information about the user adjusting a volume of theelectronic device.
 3. The volume adjusting device of claim 2, whereinthe noise reception unit receives the noise from a point of time atwhich the electronic is turned on, or receives the noise when avariation in the volume of the electronic device is at least a presetthreshold value.
 4. The volume adjusting device of claim 2, wherein thefacial information reception unit stores the face information of theuser when the electronic device is turned on or when a volume adjustmentoperation of the electronic device is performed.
 5. The volume adjustingdevice of claim 1, wherein the learning unit generates a deep neuralnetwork model for predicting an appropriate volume of the electronicdevice according to a status of noise around the electronic device, thetype of the video content displayed on the electronic device, and theuser viewing the video content, by using at least portion of the volumeinformation about the electronic device when the electronic device isturned on, the volume information about the electronic device adjustedand changed by the user when the content is changed, the type of thevideo content displayed on the electronic device, information about thecorrelation between the noise around the electronic device and thevolume information about the electronic device when the video content isdisplayed, and facial information about the user when a volume of theelectronic device is changed.
 6. The volume adjusting device of claim 5,comprising: a user determination unit configured to detect eitherturning on of the electronic device or changing of the video content,and determine whether a viewer viewing the video content displayed onthe electronic device is the user detected by the facial informationreception unit on the basis of the facial information, wherein when theviewer is determined to be the user, the device control unit adjusts thevolume of the electronic device to a volume suitable for the userpredicted by the deep neural network model.
 7. The volume adjustingdevice of claim 6, wherein the user determination unit detects eitherturning on of the electronic device or changing of the video content,and determines whether there are a plurality of viewers viewing thevideo content on the basis of the facial information, and when it isdetermined that there are the plurality of viewers, the device controlunit adjusts the volume of the electronic device to a volume suitablefor a first viewer among the plurality of viewers, wherein the firstviewer has a highest volume among suitable volumes predicted by the deepneural network model.
 8. A method for adjusting a volume of anelectronic device, the method comprising: receiving volume informationabout an electronic device adjusted by a user; learning a correlationbetween the volume information about the electronic device and videocontent displayed through the electronic device; storing a predictionmodel of an operation executable by the electronic device according to atype of the video content displayed on the electronic device on thebasis of the learned correlation between the volume information aboutthe electronic device and the video content; and controlling theelectronic device so that the predicted operation executable by theelectronic device is executed by the electronic device when the videocontent displayed through the electronic device is detected.
 9. Themethod of claim 8, wherein the receiving comprises: receiving noisegenerated around the electronic device; receiving volume adjustmentinformation about the electronic device when change information aboutthe video content displayed on the electronic device is generated; anddetecting facial information about the user adjusting a volume of theelectronic device.
 10. The method of claim 9, wherein the receiving thenoise comprises receiving the noise from a point of time at which theelectronic is turned on, or receiving the noise when a variation in thevolume of the electronic device is at least a preset threshold value.11. The method of claim 9, wherein the detecting the facial informationcomprises storing the face information of the user when the electronicdevice is turned on or when a volume adjustment operation of theelectronic device is performed.
 12. The method of claim 8, wherein thelearning comprises generating a deep neural network model for predictingan appropriate volume of the electronic device according to a status ofnoise around the electronic device, the type of the video contentdisplayed on the electronic device, and the user viewing the videocontent, by using at least portion of the volume information about theelectronic device when the electronic device is turned on, the volumeinformation about the electronic device adjusted and changed by the userwhen the content is changed, the type of the video content displayed onthe electronic device, information about the correlation between thenoise around the electronic device and the volume information about theelectronic device when the video content is displayed, and facialinformation about the user when a volume of the electronic device ischanged.
 13. The method of claim 12, further comprising: determining, inresponse to detecting either turning on of the electronic device orchanging of the video content, whether a viewer viewing the videocontent displayed on the electronic device is the user detectedpreviously on the basis of the facial information, wherein thedetermining comprises adjusting the volume of the electronic device to avolume suitable for the user predicted by the deep neural network model.14. The method of claim 13, wherein the determining comprises detectingeither turning on of the electronic device or changing of the videocontent, determining whether there are a plurality of viewers viewingthe video content on the basis of the facial information, and adjustingthe volume of the electronic device to a volume suitable for a firstviewer among the plurality of viewers when it is determined that thereare the plurality of viewers, wherein the first viewer has a highestvolume among suitable volumes predicted by the deep neural networkmodel.